Inference for a Generalised SBM with a Split Merge Sampler
accept propsbm with the acceptance probability alpha
Add a block move
Adjusted Rand Index
Block matrix
Block matrix
Block matrix
Block matrix
Block Model
Blocks object
plot a trace of the blocks from MCMC samples
Chinese Restaurant Process
Dirichlet distribution
likelihood of edges
Density of edges
Density of edges
Delete a block move
Dirichlet Multinomial Allocation
Draw block membership
Gibbs-like reassignment of nodes to the current set of blocks
Draw block memberships
Gibbs-like reassignment of nodes to the current set of blocks
Metropolis updates by drawing parameters
Class for edge models
Class for edge data
Bernoulli edge model
Negative-Binomial edge model
Normal edge model
Poisson edge model
get a set of evaluation plots from MCMC samples
is.sbm
Marginal likelihood model for Bernoulli distributed edges
Marginal likelihood model for Normal distributed edges
Marginal likelihood model for Poisson distributed edges
Merge blocks
merge move block merging
Merge step: parameters
Merge step - parameter merging
merge parameters
modal block assignments from MCMC samples
Multinomial block assignment
Likelihood of node assignment
plot a trace of the number of blocks from MCMC samples
Beta parameter model
Gamma parameter model
Parameter model for Negative Binomial
Parameter model for Normal Model
Parameter Matrix
Parameter Matrix
Parameter Matrix
Parameter Matrix
Parameter Matrix
Parameter Model
params S3 object
plot a trace of parameter values from MCMC samples
Plot blocks
Plot
Plot for sbm object
helper function for trace plots
mean proportion of times two nodes were in the same block under MCMC s...
Draw draw Categorical distribution
Dirichlet distribution
Simulate edges
Random Walk
Conjugate model sampler
Dirichlet process sampler
Gibbs sampling for node assignments
top level sampler function
reversible jump Markov chain Monte Carlo split-merge sampler
Class sbm
Stochastic block model object
split move using average to merge parameters
split move: blocks
split move: params
split move: params
split move: parameters
Update the block assignment of a node
Update the block assignment of a node
Update the block assignment of a node
V-measure
Inference in a Bayesian framework for a generalised stochastic block model. The generalised stochastic block model (SBM) can capture group structure in network data without requiring conjugate priors on the edge-states. Two sampling methods are provided to perform inference on edge parameters and block structure: a split-merge Markov chain Monte Carlo algorithm and a Dirichlet process sampler. Green, Richardson (2001) <doi:10.1111/1467-9469.00242>; Neal (2000) <doi:10.1080/10618600.2000.10474879>; Ludkin (2019) <arXiv:1909.09421>.